In the UDA problem, the labeled source domain is denoted as \(\mathbf {D}_{s}=\left\lbrace \left(\mathbf {x}_{i}^{s}, y_{i}^{s}\right)\right\rbrace _{i=0}^{N_{s}}\) , where \(\mathbf {N_{s}}\)  represents the number of samples with labels in the source domain, and the unlabeled target domain is denoted as \(\mathbf {D}_{t}=\left\lbrace \left(\mathbf {x}_{j}^{t}\right)\right\rbrace _{j=0}^{N_{t}}\) , where \(\mathbf {N_{t}}\)  represents the number of samples without labels in the target domain [1]}. The source domain and target domain samples conform to the joint distribution \(P_{s}\left(X^{s}, Y^{s}\right)\)  and \(Q_{t}\left(X^{t}, Y^{t}\right)\)  respectively, and \(P\ne Q\) . Our goal is to train a deep neural network \(\mathcal {F}:X^t\rightarrow Y^t\)  using source domain data with labels and target domain data, which can accurately predict target domain samples while minimizing domain shift.
